SCOPING STUDY Consultant: Prof. Georges Tadonki March 2020 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Table of Contents I. Introduction .......................................................................................................................................... 3 II. Objectives and approach of the scoping study ....................................................................................... 6 2.1 Objectives of the study ...........................................................................................................................6 2.2 Methodology and Approach ...................................................................................................................7 2.2 Extent and limitations of the study .........................................................................................................8 2.3 Structure of the report ............................................................................................................................9 2.3.1 Main Report .....................................................................................................................................9 2.3.2 Project Proposal ...............................................................................................................................9 III. Overview of critical issues, data and information needs for disaster Resilience ...................................... 10 3.1 Definition of key terms and concepts ...................................................................................................10 3.2 Challenges and innovative responses to data needs for disaster management ...................................14 IV. Scoping analysis and recommendations .............................................................................................. 26 4.1 Overview & findings ...............................................................................................................................26 Findings of the scoping analysis in ten points ..........................................................................................27 4.2 Pre-disaster data categories and variables for the PDNA ......................................................................30 4.3 Architecture of a national pre-disaster data sharing platform and system ...........................................32 4.3.1 Proposed National Framework for Pre-Disaster Data Dissemination (NFPDD) ..............................32 4.3.2 Proposed National Pre-Disaster Data Dissemination System (NPDDS)...........................................34 V. List and definitions of PDNA indicators and variables ........................................................................... 36 VI. Bibliography...................................................................................................................................... 52 Page 2 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery I. INTRODUCTION The world bank GFDRR initiated a scoping study to help address the constant demand for pre-disaster baseline datasets in countries vulnerable to disasters, and recipients of technical and financial support. Experience from the Post-disaster Needs Assessment (PDNA) process has revealed recurrent gaps in the availability, coverage, and quality of data required. It is challenging to establish an accurate picture of a baseline situation before a disaster happens at national, regional and mainly local levels. Yet, such baseline information is vital in planning response interventions and supporting recovery and resilience. The issue affects all levels of the DRM and DRR process, including the response to large-scale events, with widespread negative impacts on the economic fabric, infrastructures, and livelihoods. All stages of the disaster risk reduction process rely on quality data and information. Many studies highlight that timeliness and availability of relevant data have a significant impact on the effectiveness of response and recovery efforts. Despite notable improvements, most National statistical information systems and spatial data infrastructure (SDI) are unable to fully address these needs when disasters occur. Therefore, governments and partners engaged in the PDNA and other disaster assessments often face the challenge of establishing an actual pre-disaster situation. Such a context is complicated by the requirement to visualize and plan a speedy and robust recovery. There is a long-standing consensus for a greater availability of reliable pre-disaster datasets in some disaster-prone developing countries. It is a complex issue for the sustainable development, disaster and resilience communities, despite decades of efforts to support the national capacity for data and Page 3 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery information management activities related to pre-disaster and vulnerability assessments. It affects emergency response and the overall framework of disaster risk reduction. Similar initiatives amongst international stakeholders have led to the establishment of a growing number of data-exchange platforms such as the Humanitarian Data Exchange Platform HDX and the World Bank Open Data1. Particularly for humanitarian emergency response. However, the preparedness data needs for recovery and resilience planning pose a different challenge. This scoping study is an opportunity to explore the disaster data challenge with lasting results, using the experience accumulated from several PDNAs and other disaster assessments to support recovery and resilience activities. There is a seeding point where institutional knowledge and technology meet. It opens opportunities for highly scalable and faster databases, enhanced data mining and a better integration of geospatial data and Artificial Intelligence (AI). It also facilitates innovative approaches to data collaboration and dissemination for decision-support and awareness building, that are key in emergency management. Significant changes and innovations have taken place in sub-Saharan Africa, Latin American and South- East Asian countries. For the past two decades, the scale of access to mobile telephony and the Internet in these countries is an indication of fast change in technology and cultural attitudes. One of the results of this revolution is the availability of a significant amount of data for modeling behaviors, mobility and other social factors in disaster-prone communities. The correlation of data obtained by sensors, semantic analysis, and secondary statistical data can provide reliable information on through geographic snapshots of human economic activities before a disaster affects a community. The study will help identify the enabling factors for greater use of these resources in developing countries, in partnership with the private sector, while respecting data ethics relevant to personal privacy, safety and security in the communities. This scoping study initiated by the GFDRR’s Resilient Recovery Team makes a case for an initiative to support governments in their efforts to prepare baseline data sets for disaster assessment and recovery. It will build on an extended partnership with UNDP and the EU, learning from related work initiated in countries receiving support through the PDNA process. It identifies the needs for data (secondary and primary data, data users, and data reporting needs), defines the approach and framework to obtain these datasets, design the tools and deliverables for the intended target groups. The expected outcome is an improved national capacity to assess, prepare, respond to and recover from disasters, supported by the effective collaboration of all stakeholders and development partners. The study relies on a review of research work published in academic journals, and relevant institutional reports. The search of several databases reveals several thousand relevant references (Scopus, Elsevier, Google Scholar). The initial review of literature has unearthed a substantive amount of research papers and reports providing evidence about the complexity of data requirements for 1 Example of data portals: https://data.humdata.org https://data.worldbank.org/ Page 4 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery emergency preparedness, response, and recovery. It also shows the extent of the revolution in information technology, including Big Data developments and their potential benefits for improved preparedness, response, recovery and resilience interventions. A bibliography of the reviewed material is attached and will expand by the end of the study. A comparative field review of a sample of countries will help improve this scoping study (Africa, Latin America and Asia). Country research will allow face-to-face interaction with stakeholders, data custodians, and data producers. It will also build on the invaluable experience of special groups using and sharing data for national recovery and resilience planning efforts. For these countries, the scoping study identified vital data sources and resources for pre-disaster baseline monitoring and mapping. A country data-mapping exercise provides robust results which could be scaled-up during the implementation phase, and through capacity building. Country-based studies will help strengthen national data management frameworks for relevant indicators, datasets, and stakeholders. Their expected results include a detailed inventory of available datasets and data sources to cover the indicator needs for the PDNA process, and help prepare future post-disaster assessments, using a common and reliable information. Page 5 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery II. OBJECTIVES AND APPROACH OF THE SCOPING STUDY 2.1 Objectives of the study The purpose of this scoping study is to identify and analyze factors that facilitate or impede the timely availability of reliable pre-disaster baseline datasets in countries vulnerable to disasters. The findings will help define the scope and extent of the WB GFDRR technical and financial support initiatives aimed at reinforcing national capacity to prepare, manage, and disseminate baseline datasets in a timely way. Pre-disaster datasets are essential for rapid and effective post-disaster needs assessments and the planning of recovery frameworks and assistance. The core objective of needs assessments after disasters is to compare pre-disaster conditions with post-disaster impacts by sector of activity and livelihoods, at micro and macroeconomic levels. In doing so, governments and donors can visualize the impacts of the disaster and the needs for recovery. In the case of the education sector, typical pre-disaster indicators and data required may include the number of educational infrastructures in the affected area (urban and rural), the construction type of individual schools (dimensions, building materials, capacity, number of stories, etc.), whether ownership is public or private, and by educational level. The literacy rate and gender balance in schools are also essential indicators. A post-disaster gap analysis based on this type of data provides the government and development partners a clear picture of how many schools are partially damaged or destroyed. It may also show the rate of students dropping out and their gender. Such analysis helps the government target its interventions to build back better. A national school map is designed to Page 6 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery respond to these data and analytical needs. However, it may be affected by capacity and financial gaps. Demographics are similarly affected in developing countries. Generally, data on the informal sector activities are incomplete. Yet it is a significant sector of activity in vulnerable countries and communities, a key factor of recovery and resilience. Pre-disaster data should be disaggregated to the smallest geographical or administrative unit possible, to reflect the context of affected communities, before and after disaster events. Yet, such data is often missing or not readily available in vulnerable countries, no matter their level of economic development. Many issues contribute to the situation, resulting in delayed assessments or forcing assessment teams to accept a low threshold of data quality. Agencies are compelled by the urgency and the acute needs in affected communities. However, specialists are aware that issues affecting the completeness, availability, and accessibility of pre-disaster data are likely to affect the quality of the response. Limited data may also impact the allocation of financial resources and support to sectors, regions or communities in need. The report reviews these issues. 2.2 Methodology and Approach The DRM and DRR communities are aware of the challenges described in the objectives of the study. Thousand peer-reviewed research papers cover the topic. Therefore, this study is not another analysis of disaster data needs. Instead, it is a practical approach to explore data solutions adapted to countries that are recipients of the World Bank and donors' financial support for disaster recovery and resilience. As such, the study provides an overview of pre-disaster data management practices, existing gaps; the potential demand for improved data; and a proposal for a Pre-Disaster Baseline Dataset Project. The study is an attempt to identify available data, the gaps, the potential demand, and the design of pre-disaster baseline datasets. It explores the benefits of adopting standards for data collection and dissemination amongst disaster risk management stakeholders for preparedness and recovery. Lessons-learned will be applied in pilot countries to test the capacity to deliver baseline datasets for all sectors covered by the PDNA process. Pilot studies will help compile and share national baseline datasets (disaggregated to the district and urban settlement levels). An annual update is recommended using a collaborative approach and adapted technology. The scoping study compiled key data and indicators for the PDNA, and their sources. It reviewed the main issues impeding availability and quality. It highlights practical approaches, tools, templates, and system for data collection and information management and sharing. It includes actionable recommendations to address identified gaps and to scale up/implement technical and financial support to countries. The extensive literature review covered data issues and approaches to data management and applications for disaster risk management, with a focus on vulnerability analysis and mapping, SDGs data and indicators, preparedness, response, and recovery. It relies on a reasonable amount of relevant references, including legislations, policies, reports and research papers. A sample of related academic research papers were identified using global databases and libraries: Scopus, EBSCO, Google Scholar, ProQuest, JSTOR, LexisNexis Academic and Elsevier. Institutional reports and documents were Page 7 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery collected from the World Bank and AFDB, donor agencies such as ECHO and DFID, NGOs, national governments and agencies, The United Nations, including UNDP, OCHA, UNHCR, WHO, WFP and FAO. The study also reviewed documents and reports related to the PDNA process in countries covered by the World Bank’s technical and financial assistance. Interviews and discussions with key informants provided additional inputs to the analysis. The scoping study includes a project proposal for capacity building in vulnerable countries to support the regular data collection, management, analysis and dissemination of relevant pre-disaster datasets. It follows and indicator-based approach, aligned with SDGs, the Sendai Framework recommendations and national objectives for resilience and Building Back Better (BBB). 2.2 Extent and limitations of the study Given the complexity of the study, the variety of needs for country support, and the limited time provided, the report covers issues through sectors, themes, and geographic categories. It is not an exhaustive review of the situation. The study is limited to pre-disaster data requirements for the PDNA and recovery planning processes, although the benefits extend to the entire DRM and DRR practice in vulnerable countries. Developing countries are the main target of the study. However, these countries present different realities. It is a critical factor in data and information management. Therefore, the study analyses issues in countries presenting a similar profile and presents results into relevant groupings. The same caution applies to recommended solutions and approaches. Each country requires a customized plan and tools to meet its pre-disaster data requirements. For example, what worked in South Africa may not work in Senegal. Likewise, what works in Mexico may not work in Nicaragua. However, data standards are generally universal. Therefore, a common framework may manage various types of data and indicators — for example, geospatial data standards. The study relies on a literature review of a sample of reports and scientific papers relevant to the issue of effective disaster data management to support all phases of DRM and DRR. Country studies should collect more contextual research, as they often explore a broader range of specific issues and solutions. Research conducted in vulnerable countries may also contain a substantive amount of data relevant to establishing a baseline situation at the national or sub-national level. The study will not attempt to map pre-disaster data requirements and datasets for all countries in need. However, it will provide a flexible framework of analysis and support to identify and address potential gaps. The scoping study did not use Focus Group Discussion. However, they could be part of country-specific studies. A field mission is recommended in relevant countries selected in different regions. It will allow complete mapping of relevant indicators, data sources, available data, quality, gaps and a proposal for support interventions. Page 8 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery 2.3 Structure of the report Based on the initial desk-review of reports and relevant scientific literature with the study requirements in view, the following is a simple structure for the report. 2.3.1 Main Report Main Report Introduction I. Objectives and approach of the scoping study ➢ Objectives & extent of the scoping study ➢ Definitions, data standards for disaster risk management ➢ Methodology, Approach, Constrains and Solutions Used II. Overview of key issues, data and information needs for disaster resilience ➢ Review of pre-disaster baseline data and indicator requirements for vulnerable countries (Indicators & Datasets) ➢ Capacity mapping of National information management resources for disaster assessment and recovery planning (Relevant data sources, Providers and Users) ➢ Baseline datasets for pre-disaster situation analysis (Available & reliable indicators & datasets) III. Scoping analysis and recommendations 3.1 Findings and recommendations ➢ Recommendations and guidance for the PDNA and disaster data needs assessments in vulnerable countries 3.2 Design proposal for a national pre-disaster data sharing system ➢ Architecture of a national pre-disaster data sharing platform and system Annexes Bibliography 2.3.2 Project Proposal Project Proposal Proposal of support activities to increase the availability, dissemination and use of Pre-Disaster Baseline Datasets in vulnerable countries ➢ Project proposal: A Five-year support program to reinforce national capacity for maintaining a data-sharing platform for pre-disaster indicators and datasets in developing countries. Page 9 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery III. OVERVIEW OF CRITICAL ISSUES, DATA AND INFORMATION NEEDS FOR DISASTER RESILIENCE 3.1 Definition of key terms and concepts There are many definitions and interpretations of disaster risk management terms. Therefore, the first step of a review of data needs is to clarify the terms and concepts used in the study. The following definitions set the basis for a discussion about pre-disaster data needs for disaster risk management and resilience. It also refers to the overall framework of the disaster risk management cycle2. For UNDRR, “Disaster Risk Management is the systematic process of using administrative directives, organizations, and operational skills and capacities to implement strategies, policies, and improved coping capacities in order to lessen the adverse impacts of hazards and the possibility of disaster”3. Also, to summarize multiple definitions gathered from national agencies around the world, a disaster is the occurrence of a natural catastrophe, technological accident or human-caused event resulting in severe property damage, deaths, and/or multiples injuries. A “major disaster” or “large-scale disaster 2 The definition of terms and concepts used in this scoping study do not reflect a position of the World Bank or the GFDRR 3 UNDRR: United Nations Office for Disaster Risk Reduction Page 10 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery is one that exceeds the capability of the affected communities to respond and cope with its impacts. It requires State intervention, possibly with international assistance to save lives and alleviate suffering4. A civil protection system in a country is the coordinated framework of laws, plans, organizations, stakeholders, people and resources working together to protect civilians, assets, and economic activities against the negative impact of natural or human-made disasters and accidents. A civil protection organization is any state, volunteer, or non-governmental entity assuming one or many responsibilities in the functions of a civil protection system at national, regional, local, or international levels. The Civil Protection system is a primary beneficiary of greater availability and quality of pre- disaster datasets. The scope of disaster management is relatively broad in terms of policy, practice, and research. Civil protection carries a substantive operational expectation. It is reflected in the timeliness or immediacy of urgencies, and the imperative to save lives, alleviate suffering and protect economic assets in communities affected disasters. While there is an argument about such distinctions, the immediate operational nature of civil protection systems is recognized by most practitioners and analysts. It becomes clear when disaster strikes, that unprepared communities pay a higher price, aggravated by the accumulated gaps in emergency preparedness, response, recovery planning, and mitigation. For citizens and communities at risk, the proximity and availability of emergency services are part of their sense of safety and security. A community may have a local disaster management plan in place, how it is updated, and eventually used to respond to disasters and recover from it depends on its overall readiness. A community struggling to respond to minor emergency incidents is likely to suffer more significant damages and experience a slower recovery in the aftermath of big disaster events. There is a readiness continuum entailing institutional capacity, the participation of all actors, financial resources and the technical capability to cope with minor and major incidents. The availability of reliable data and information plays a critical role in the disaster risk reduction cycle, and developing resilience. The concept of resilience used in this study refers to “the ability of countries, communities, and households to manage change, by maintaining or transforming living standards in the face of shocks or stresses – such as earthquakes, drought or violent conflict – without compromising their long-term prospects”5. Resilience is also “The ability of a system, community or society exposed to hazards to resist, absorb, accommodate, adapt to, transform and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions through risk management”6. There is an interagency understanding of recovery as “the restoration, and improvement where appropriate, of facilities, livelihoods and living conditions of disaster-affected communities, including efforts to reduce disaster risk factors.” The recovery task of rehabilitation and reconstruction begins 4 Adapted from FEMA and several other national disaster management authorities. 5 DFID (2011a). Defining Disaster Resilience: A DFID Approach Paper. DFID, 2011, 6 https://www.undrr.org/terminology/resilience Page 11 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery soon after the emergency phase has ended. It is based on pre-existing strategies and policies and gradual adoption of the “build back better” principle in most countries. The objective of this study is not a review of the disaster risk management framework. However, two policy processes are considered foundational to the study: the Post-Disaster Needs Assessment (PDNA), and the Disaster Recovery Framework (DRF). The DRF is a crucial process established through a multistakeholder experience of recovery planning in countries receiving post-disaster international assistance. It aims at aligning institutional partners with a standard post-disaster assessment approach and recovery planning strategy. Within the DRF, the Post-Disaster Needs Assessment (PDNA) exercise provides quantified and validated evidence for response and recovery planning. There is a broad interagency agreement for strengthening the disaster recovery framework for resilient recovery7. Maximizing the potential of PDNAs is a key step toward achieving that strategic objective. Figure 1 The Disaster Recovery Framework DRF process (Source: Guide to developing disaster recovery frameworks, Sendai Conference Version, WB GFDRR, UNDP, UE, Mar. 2015) The PDNA has considerably evolved as a multi-stakeholder process supporting the evidence-based policy and decision-making approach in disaster risk management (Figure 2). However, depending on the nature of the emergency, its complexity increases the challenge of timely recovery planning and implementation. Time is a critical factor in the relation between the PDNA and the DRF (Figure 3). Therefore, reaching a working consensus on a baseline situation as early as possible will benefit both processes. This requirement is frequently mentioned in PDNA reports and related discussions about the availability of reliable data in affected countries to establish a baseline situation before a disaster 7 Guide to developing disaster recovery frameworks, Sendai Conference Version, WB GFDRR, UNDP, UE, March 2015 Page 12 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery happens. Post disasters, a considerable amount of time is taken from responders and recovery planners, collecting and collating baseline data. Figure 2 The relationship between the DRF and the PDNA (Source: Guide to developing disaster recovery frameworks, Sendai Conference Version, WB GFDRR, UNDP, UE, Mar. 2015) Page 13 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery 3.2 Challenges and innovative responses to data needs for disaster management In the aftermath of a disaster, the lack of baseline data may create several risks and biases which impact the emergency response, with a cumulative effect on recovery planning and resilience. CARE reported these challenges when responding to the 2014 floods in Bangladesh8. The lack of pre-disaster data and multiple humanitarian assessments increased the difficulty of establishing a reliable overview of the situation. In the absence of standards and information-driven coordination strategies, rapidly overlaying information from non-concordant sources and varying reliability could become a burden. It is valid during emergencies, affecting early and long-term recovery efforts. Figure 3 Bangladesh Floods 2014, Many sources of data of variable quality (Source: van den Homberg, M., Monné, R., & Spruit, M., 2018. Bridging the information gap of disaster responders by optimizing data selection using cost and quality. Computers & Geosciences, 120, 60–72.) Typically, while data gaps are observed in the early phase of the emergency, as the situation evolves with the intervention of many responding organizations and stakeholders, there seems to be too much data9. When facing such information burden, emergency responders and managers may rely on experience to maximize response and recovery efforts, based on established priorities. This simplified decision-making process is affected by cognitive and motivational biases10. It could lead to some needs being overreported through needs assessments and appeals, while agencies underreport others. The cognitive bias has far-reaching implications on effectiveness, when a simplified mental model is used to address the complex needs of some disasters11. Confronted with the complexity of a disaster situation in vulnerable countries, interagency coordination aims at minimizing cognitive and motivational biases, particularly during needs assessments. Emergency planners must balance knowledge of crisis impact and the operational environment (Figure 4). 8 Care Bangladesh, 2014. Plan for Arriving at a Shared Understanding of Flooding in Bangladesh. (Unpublished report). 9 van den Homberg, M., Monné, R., & Spruit, M., 2018. Bridging the information gap of disaster responders by optimizing data selection using cost and quality. Computers & Geosciences, 120, 60–72. Page 14 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Figure 4 List of information needs In many ways, the framework for disaster risk management determines the orientation, depth, and quality of needs assessments. The lack of transparency and gaps in governance may impede the availability and sharing of baseline data. In all phases of planning and operations, the goal of recovery and resilience is a bridge between emergency risk management policies and expectations of intervening stakeholders. The linkages between urban DRR and SDGs reflects these issues. Risk 10 Montibeller, G., von Winterfeldt, D., 2015. Cognitive and motivational biases in decision and Risk analysis. Risk Anal. 35, 1230–1251. https://doi.org/10.1111/risa.12360. 11 Comes, T., 2016. Cognitive biases in humanitarian sensemaking and decision-making lessons from field cognitive biases in humanitarian sensemaking and decision-making lessons from field research. In: 2016 IEEE Int. Multi-disciplinary Conf. Cogn. Methods Situat. Aware. Decis. Support. CogSIMA 2016, pp. 56–62. Page 15 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery identification and mapping activities contribute to these processes, leading to a sustainable recovery, rehabilitation, and reconstruction planning. Figure 5 Preparedness and Urban resilience in the broader framework of SDGs - SFDRR 2015-2030 (Source: Etinay N., Egbu C., Murray V., 2017, Building Urban Resilience for Disaster Risk Management and Disaster Risk Reduction, 7th International Conference on Building Resilience; Using scientific knowledge to inform policy and practice in disaster risk reduction, ICBR2017, 27 – 29 November 2017, Bangkok,) A stakeholder mapping exercise outlines the institutional and coordination framework for disaster risk management in a country. It helps understand the gaps, constraints, and enabling factors. However, each country presents a specific case. An assessment of the policy and regulatory framework of Tonga's capital Nuku'alofa provides an opportunity to explore the issue12. The study used an indicator-based approach to review and rate policy legislation and institutional arrangements in seven areas: Risk Governance, Disaster Response, Post Disaster Rehabilitation and Recovery, Risk Finance, Urban development, and Monitoring and Evaluation. It noted the lack of standard assessment methodology to collect data on losses and damages in the pacific island country. However, there is a commitment to strengthen national capacity and Build Back Better (BBB). In this regard, Tonga joined the Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI), a public-private partnership for resilience. 12 Fakhruddin, B. (SHM), Reinen-Hamill, R., & Robertson, R. (2019). Extent and evaluation of vulnerability for disaster risk reduction of urban Nuku’alofa, Tonga. Progress in Disaster Science, 2, 2019. Page 16 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Figure 6 Vulnerability assessment ranking of key areas of the DRM framework in Tonga (Source: Fakhruddin, B. (SHM), Reinen-Hamill, R., & Robertson, R. (2019). Extent and evaluation of vulnerability for disaster risk reduction of urban Nuku’alofa, Tonga. Progress in Disaster Science, 2, 2019). Page 17 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Several other studies attempt to assess and rate resilience frameworks (Figure 7). Such studies rely on secondary data and Focus Discussion Groups. The multi-risk and multi-hazard approaches are rooted in community-based disaster risk management13. Some rural and urban communities have developed a social support infrastructure that enhances self-reliance in case of disaster. These communities maintain a sophisticated knowledge of their members, threats, and vulnerabilities. In West and Central African cities, women are organized into associations that can rapidly compile information on losses and damages suffered by their members, estimate the needs for reconstruction and recovery. Understanding these informal structures and civil society is key to strengthening resilience in vulnerable communities. Figure 7 Social resilience frameworks (Source: Aslam Saja A. A., Goonetilleke A., Teo M., Ziyath A. M., 2019, A critical review of social resilience assessment frameworks in disaster management, International Journal of Disaster Risk Reduction, Volume 35.) The complexity of the decision-making process in disaster management is manifest in humanitarian logistics14. Agencies must deliver assistance to affected communities in a timely and effective way to save lives and minimize economic losses and their impact on livelihoods15. These operations require a considerable amount of data. In that context, supply chain risk management is the systematic 13 Aslam Saja A. A., Goonetilleke A., Teo M., Ziyath A. M., 2019, A critical review of social resilience assessment frameworks in disaster management, International Journal of Disaster Risk Reduction, Volume 35. 14 Iqbal, S., Sardar, M. U., Lodhi, F. K., & Hasan, O. (2018). Statistical model checking of relief supply location and distribution in natural disaster management. International Journal of Disaster Risk Reduction, 31, 1043–1053. 15 Sabbaghtorkan, M., Batta, R., & He, Q. (2019). Prepositioning of assets and supplies in disaster operations management: Review and research gap identification. European Journal of Operational Research. Page 18 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery identification, analysis, and mitigation of risks that could affect supply chain networks16. Partial or complete interruption of a supply chain network negatively impacts businesses in the affected area and beyond. A disaster will disrupt the normal functioning of supply chains, increasing uncertainty and stress on companies and affecting end consumers, especially for critical supplies17. Decision-support systems help plan and implement business-continuity in a public-private partnership. Logistical disaster management relies on operations research (OR) and management sciences (MS)16. Figure 8 A Framework integrating social media and authoritative data for disaster relief detection, distribution and optimization (Source: Schempp T., Zhang H., Schmidtc, Minsung Hong A., Akerkar R., 2019, A framework to integrate social media and authoritative data for disaster relief detection and distribution optimization, International Journal of Disaster Risk Reduction Volume 39, October 2019.) One of the benefits of decision-support systems (DSS) and Artificial Intelligence (AI) is the ability to process a considerable amount of data (Big Data) in quasi-real-time (Figure 9 and 10). AI adds the possibility of using machine learning to explore multiple dimensions in data18. These areas of research 16 Boonmee, C., Arimura, M., & Asada, T. (2018). Location and allocation optimization for integrated decisions on post- disaster waste supply chain management: On-site and off-site separation for recyclable materials. International Journal of Disaster Risk Reduction, 31, 902–917. 17 Schätter, F., Hansen, O., Wiens, M., & Schultmann, F. (2019). A decision support methodology for a disaster-caused business continuity management. Decision Support Systems, 118, 10–20. 18 Jamali, M., Nejat, A., Ghosh, S., Jin, F., & Cao, G. (2019). Social media data and post-disaster recovery. International Journal of Information Management, 44, 25–37. Page 19 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery continue to show significant innovations with promises to disaster risk management, considering the frequency and complexity of recent disasters, in a planet marked by demographic and environmental pressure19 20. There are many applications of DSS and AI using mobile data and social media21. Research Figure 9 oDMN metmodel for Brazil (Source: Horita F.E.A., Porto de Albuquerque J., Marchezin V., Mendiondo E. M., 2017, Bridging the gap between decision-making and emerging big data sources: An application of a model-based framework to disaster management in Brazil, Decision Support Systems 97 (2017) 12–22) 19 Sadhukhan, S., Banerjee, S., Das, P., & Sangaiah, A. K. (2018). Chapter 9 - Producing Better Disaster Management Plan in Post-Disaster Situation Using Social Media Mining. In A. K. Sangaiah, M. Sheng, & Z. B. T.-C. I. for M. B. D. on the C. with E. A. Zhang (Eds.), Intelligent Data-Centric Systems (pp. 171–183). Academic Press. 20 Joseph, J. K., Dev, K. A., Pradeepkumar, A. P., & Mohan, M. (2018). Chapter 16 - Big Data Analytics and Social Media in Disaster Management. In P. Samui, D. Kim, & C. B. T.-I. D. S. and M. Ghosh (Eds.) (pp. 287–294). Elsevier 21 Theja Bhavaraju, S. K., Beyney, C., & Nicholson, C. (2019). Quantitative analysis of social media sensitivity to natural disasters. International Journal of Disaster Risk Reduction, 39, 101251. Page 20 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery has provided an option for extracting information from social media to identify victims, the most affected areas, optimize the distribution and allocation of relief resources during emergencies22. Data mining applications have a potential for prediction, detection, risk mapping and analysis, feeding into the preparedness, response, and recovery phases23. Saptarsi Goswami and his colleagues provide a detailed review of data mining applications using big data (Figure 10). Globally, Twitter is one of the most explored data sources for such applications and their data models. In 2019, Twitter averaged 330 million monthly active users (Tweets are short messages, geocoded. These characteristics amongst others, present many opportunities for data mining). However, other platforms offer similar potential such as Weibo in China24. In their work, these researchers also considered the data dimensions of volume (GIS data, meteorological data, social media data), variety (text, time series, spatial data, GIS images), and velocity (the rate at which data is generated and speed required for a decision). They attempted to map the type and sources of data needs for the disaster risk management process. Figure 10 Cloudburst data, model and task summary (Source: Goswami S., Chakraborty S., Ghosh S., Chakrabarti A., Chakraborty B., 2018, A review on application of data mining techniques to combat natural disasters, Ain Shams Engineering Journal, Volume 9, Issue 3, September 2018, Pages 365-378.) 22 Schempp T., Zhang H., Schmidtc, Minsung Hong A., Akerkar R., 2019, A framework to integrate social media and authoritative data for disaster relief detection and distribution optimization, International Journal of Disaster Risk Reduction Volume 39, October 2019. 23 Goswami S., Chakraborty S., Ghosh S., Chakrabarti A., Chakraborty B., 2018, A review on application of data mining techniques to combat natural disasters, Ain Shams Engineering Journal, Volume 9, Issue 3, September 2018, Pages 365- 378. 24 Shan S., Zhao F., Wei Y., Liu M., 2019, Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter), Safety Science,Volume 115, June 2019, Pages 393-413 Page 21 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Figure 11 Data types and sources for disaster risk management (Source: Goswami S., Chakraborty S., Ghosh S., Chakrabarti A., Chakraborty B., 2018, A review on application of data mining techniques to combat natural disasters, Ain Shams Engineering Journal, Volume 9, Issue 3, September 2018.) As more households use mobile phones and connect to the Internet in developing countries, social media platforms generate a considerable amount of data. It can be used in all phases of disaster risk management, before and after disasters happen. Notably, in the case of major emergencies, which cause considerable disruption and generate an essential flow of information between affected people and their multiple relationships. If big data is accessible in a country, there are a few constraints, such as the privacy and safety of data that raise ethical issues. Also, there is a need for more local capacity to analyze these data streams and demand for data for evidence-based emergency response and recovery. In many cases, the lack of transparency, governance, and cultural constraints may impede access to technological innovations in data science. A post-disaster comparison of the 2010 Haiti earthquake medical data with the country’s reconstituted baseline medical data provides an intriguing overview of what happens in many developing countries25. Such comparative analysis could help understand the challenges and improve preparedness, the transition between acute response to recovery, and linking emergency interventions to long-term support for recovery and resilience. 25 Berlaer G., Staes T., Danschutter D., Ackermans R., Zannini S., Rossi G., Buyl R., Gijs G., Debacker M., Hubloue I, 2017, Disaster preparedness and response improvement: comparison of the 2010 Haiti earthquake-related diagnoses with baseline medical data, European Journal of Emergency Medicine, 2017, 24:382–388 Page 22 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Figure 12 Evolution of trauma vs nontrauma patients, and infectious cases, post-2010 Haiti Earthquake (Source: Berlaer G., Staes T., Danschutter D., Ackermans R., Zannini S., Rossi G., Buyl R., Gijs G., Debacker M., Hubloue I, 2017, Disaster preparedness and response improvement: comparison of the 2010 Haiti earthquake- related diagnoses with baseline medical data, European Journal of Emergency Medicine, 2017) Most countries vulnerable to disasters are adopting the Build Back Better (BBB) concept26. It is a data- intensive approach, which relies on the implementation and maintenance of standards. There is a high demand for capacity building and the transfer of technologies in BBB projects. A review of data needs for disaster risk management leads to geospatial data and GIS27 28. There is a growing interest in Open Source software, as commercial software licensing costs are often beyond the resources of research and disaster risk management in developing countries. Free or Open Source Software (FOSS) programs provide free access to the latest technology in geospatial data management and spatial analysis. Added to this is also the ability to use open-source operating systems such as Ubuntu. However, organizations making the shift to open-source software must overcome a few challenges. The choice of the right free software is the first challenge. The next one is assuming the cost of technical support. While Open Source software is indeed free, the learning curve is often a challenge. Organizations must factor the cost of support to ensure the smooth and efficient use of these tools. Initially, overlooked support costs can quickly escalate beyond the cost of commercial licensing for GIS. 26 Glenn F., Iftekhar A., 2019, “Build back better” approach to disaster recovery: Research trends since 2006, Progress in Disaster Science, Volume 1, May 2019 27 Leidig M., Teeuw R., 2015, Free software: A review, in the context of disaster management, International Journal of Applied Earth Observation and Geoinformation, Volume 42, October 2015, Pages 49-56. 28 Council, N. R., Studies, D. E. L., Resources, B. E. S., Committee, M. S., & on Planning for Catastrophe: A Blueprint for Improving Geospatial Data, T. I. (2007). Successful Response Starts with a Map: Improving Geospatial Support for Disaster Management. National Academies Press. Page 23 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery The revolution in geospatial data makes available online an unprecedented amount of freely accessible data from satellites and other sensors. For vulnerable countries affected by disasters, the UN International Charter Space and Major Disasters provides access to remote sensing and geospatial data. The Charter also provides links to data processing resources and teams in the world closer to the request29. A growing number of volunteers are mapping the world, delivering vital geospatial data for places previously out of the map. Volunteered Geographic Information (VGI) is user-generated data in the form of location, geotags, and attributes30. The Human-centric approach maps kind of human activity, mobility, relations, and perceptions. The application-centric approach contributes to crisis-detection and prediction, monitoring, response and recovery, health and coordination31. OpenStreetMap OSM is a collaborative mapping initiative bringing together about 5.7 million users. The maps are editable and freely available32. The latest versions of FOSS GIS software such as QGIS can automatically connect to hundreds of online map services to download, edit, and analyze open geospatial data. These opportunities can clear some of the constraints to baseline data for disaster risk management in developing countries. However, there are issues with accountability and the quality of free geographic information. Data and transparency33 initiatives are delivering online data platforms with great potential for disaster risk management. The World Bank open data gives access to a broad coverage of themes, through space and time34. UNDP maintains data portals relevant to the issue of baseline data in vulnerable countries. The UNDP transparency Portal covers 149 countries35. OCHA Humanitarian Data Exchange portal provides a platform for sharing data, and beyond addressing the issues around data standards and data quality collaboratively36. More platforms are offering free access to data for disaster risk management. Technology also expands the possibilities, mainly through a multitude of new sensors. Microsatellites and drones are increasingly providing more capacity to emergency responders and planners. This scoping study identifies and highlights opportunities for data and innovations, which could help reduce the gap in data before a disaster happens in vulnerable countries. It does not intend to provide an exhaustive review of these possibilities. Country specific studies will provide a detailed account of how these initiatives may help locally. 29 International Charter Space and Major Disasters - https://disasterscharter.org/ 30 Goodchild, M. (2007). Citizens as sensor: The world of volunteered geography. GeoJournal, 69(4), 211–221. 31 Granell, C., & Ostermann, F. O. (2016). Beyond data collection: Objectives and methods of research using VGI and geo- social media for disaster management. Computers, Environment and Urban Systems, 59, 231–243. 32 https://www.openstreetmap.org/ 33 Modéer U., 2018, Data and transparency, our past and our future, UNDP Blog, September 2018 34 https://data.worldbank.org/ 35 https://open.undp.org/#2017/filter/operating_unit-IRQ/focus_area-6/donor_countries-DEU 36 https://data.humdata.org/ Page 24 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery One aspect often overlooked is the relationship between scientific innovation and decision-making in disaster risk management. Advances and innovations in research do not always reach decision-makers as expected37. Likewise, progress made in the coordination mechanisms of disaster management could be made more evident to the scientific community. Scientific research provides new ways to address missing data and data gaps issues using modeling approaches and tools. Modern technologies allow a faster and more in-depth exploration, analysis, and reporting of damages and losses. It supports robust planning of response, recovery, and resilience. Developing countries can harness the opportunities offered by scientific innovation to address data and information challenges in their disaster risk management strategies. Open source and open data concepts are new approaches to support the production and dissemination of public data faster than ever before. However, it requires a deeper understanding of the situation and involvement in developing countries. Furthermore, in most developing countries, urban landscapes are characterized by a high degree of informal development, reflected in informal urban sprawl and economic activities. Yet, capturing informal activities remains a challenge for national statistical systems and spatial data infrastructure (SDI). 37 Schempp T., Zhang H., Schmidtc, Minsung Hong A., Akerkar R., 2019, A framework to integrate social media and authoritative data for disaster relief detection and distribution optimization, International Journal of Disaster Risk Reduction, Volume 39, October 2019. Page 25 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery IV. SCOPING ANALYSIS AND RECOMMENDATIONS 4.1 Overview & findings Establishing a pre-disaster baseline context is the first step of the PDNA and other post-disaster assessments. It is a time-consuming task, which requires the compilation of baseline datasets. In many cases, such data is not readily available at the onset of a disaster. Figure 13 PDNA baseline situation and data requirements (Source: GFDRR) Page 26 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Figure 14 PDNA data requirements (Source: GFDRR) Three focus areas emerge for interventions to support quick, effective, and coordinated assessment & recovery (PDNA and DRF). Focus area 1. Relevant and Reliable Context Analysis: identify, describe, and quantify the damages, losses & needs of affected communities. Focus area 2. Effective & Inclusive Recovery Planning: prioritization and sustainable results, Building Back Better (BBB), and resilient communities. Focus area 3. Resource Mapping Exercise: identify and share relevant & quality Data, including Pre- disaster data. Findings of the scoping analysis in ten points 1. Most studies & reports concur on data gaps: availability, coverage, quality & timeliness Most PDNA reports share concerns about the lack of baseline data. Disaster studies confirm that situation. Generally, countries in the category of “Developing Civil Protection System” have accumulated a substantive amount of and information to establish a baseline situation in case of disaster38. However, the challenges in coordination impede dissemination and the implementation of standards. In such cases, the local cultural attitude towards data and governance is a crucial constraint or an enabling factor for evidence-based decision-making. These countries offer the best opportunities for capacity building, investment in data and simulations. 38 World Bank, Global Facility for Disaster Reduction and Recovery GFDRR, 2019, State of Civil Protection in The World: Typologies, Good Practices & Economic returns, Report, Washington, DC, May 2019 (Unpublished) Page 27 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery 2. Data issues reflect gaps in national statistical systems and spatial data infrastructures The development of a national statistical system does not systematically lead to the high availability of disaggregated data. When a disaster happens, the gaps in baseline information are likely to reflect challenges in the system. The same issues will affect the availability of granular spatial data, despite the extensive coverage of global geospatial data sources. The UN International Charter Space and Major Disasters addresses some of these challenges by providing historical data along with the latest satellite imagery and analytical products for the affected area. However, to establish and maintain a reliable baseline description of vulnerable communities and relevant data layers, it is urgent to support national data and monitoring efforts. 3. Formal vs. Informal: informal situations present the highest gaps in data Countries with a high level of informal economic activity and spatial development pose a unique challenge to traditional statistical systems. Given the fast urbanization and urban risks, understanding and accounting for the billion people living in informal settings is key to disaster risk management. Yet, despite the apparent vulnerability, informality offers a lot to learn for resilience and the self-organizing capacity of communities. Community-based structures are an invaluable source of data and information. 4. Decision-making: the culture of data versus ad hoc responsive culture Modern emergency management is a data-intensive process. Challenges in coordination and governance often reflect a lack of transparency and decision-making processes that rely less on data. Countries in that situation are vulnerable to motivational biases affecting long-term recovery efforts. 5. Humanitarian interventions increase the availability of data before and after a disaster There are considerable data and information in countries with a sizeable humanitarian presence. International and local humanitarian actors conduct a high number of surveys and other forms of systemic data collection for their programming as well as monitoring and evaluation. Moreover, the humanitarian reform resulted in a major improvement in the practice of information management for international emergencies. The cluster system facilitates the regular monitoring of vulnerable communities, the publication of situation reports, context analyses and humanitarian appeals, which contain valuable information for PDNAs and recovery planning. The Humanitarian Data Exchange Platform (HDX) provides a remarkable picture of coordination and technology for data dissemination. However, its data repositories lack disaggregated data for some African countries, at the level required for recovery planning. 6. Greater coordination requires data and supports data availability As coordination for disaster risk management improves in a country, there is a higher demand for data. One challenge is ensuring that standards are implemented for data collection, management, analysis, reporting, and dissemination. In many situations, available data is not Page 28 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery reliable enough for recovery programming. It may also increase the burden of data cleaning and interpretation. 7. Progress in data standards and tools Today, the global disaster risk management community can use many data standards to improve the quality of the information produced and its dissemination. The remaining issue if the capacity of national civil protection systems to choose systems and tools adapted to their context. Such a decision is made harder by the abundance of options. 8. Advances in data modeling to interpolate missing data Data modeling and AI offer new opportunities for dealing with situations with incomplete and missing data. The ability to connect the dots with existing data has never been more exceptional. The same approach and tools can help prepare baseline data and a contextual narrative for affected areas. It will require investment to support the training and use of data scientists in vulnerable countries. It also requires efforts to promote data transparency and governance culture in those countries. 9. The increased ecosystem of data operatives and specialized clusters in countries A community of scientists and data-aware disaster management specialists is emerging in vulnerable countries. It constitutes the foundation for data support and the production of a baseline narrative and datasets. 10. More sensor data are freely available: geospatial applications, Open Source data, including Artificial Intelligence (AI) There is an abundance of remote sensors, satellites, free geospatial data, and online data platforms. Developing countries could use these data resources and open-source software to create and maintain an updated risk map. It presents a multihazard baseline data and analysis for all areas vulnerable to disasters. Page 29 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery 4.2 Pre-disaster data categories and variables for the PDNA The PDNA process collects and analyzes data from a sectoral approach covering the sectors and sub- sectors affected by a disaster. Indicators and variables are arranged into groups and sub-groups (Table 1). The data dimensions include geography, type, and unit of measure. Each PDNA report covers sectors specific to the disaster situation. There are many lists of sectors, sub-sectors, indicators, and variables recommended for the PDNA, but no central repository or dictionary of indicators and variables. Also, in some cases, different definitions and units of measure are used for the same indicators and variables. Baseline data requirements and standards are aligned with the assessments for comparison and impact analysis. PDNA data categories and sub-categories SECTOR SUB-SECTOR Productive Agriculture Productive Forestry Productive Livestock Productive Poultry Productive Fishery Productive Commerce and Industry Productive Tourism Social Housing & Human Settlements Social Education Social Health Social Cultural heritage Infrastructure Water and sanitation Infrastructure Electricity - Energy Infrastructure Transport Infrastructure Community Infrastructure Infrastructure Communications Cross-sectoral Governance Cross-sectoral Employment & Livelihood Cross-sectoral Environment Cross-sectoral Gender Cross-sectoral Disaster Risk Reduction Table 15 PDNA data categories and sub-categories (Source: PDNA reports and GFDRR) Page 30 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery The PDNA also relies on multiple layers of secondary data and contextual analysis, including geospatial data, survey studies, econometric studies, and economic reports, environmental analysis, humanitarian analysis, as well as thesis and research papers. A data dictionary and a global repository of indicators and variables for the PDNA will support the process. National databases could synchronize their indicators and standards with the global PDNA Indicator repository for consistency. A global dictionary also provides a quick and standard way to lookup for indicator definitions. Page 31 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery 4.3 Architecture of a national pre-disaster data sharing platform and system The challenge of timely and reliable pre-disaster data in vulnerable countries is acute. To overcome the obstacles, it is crucial to find optimal ways to ensure that existing data are discoverable, collectable, manageable, sharable and easily accessible in these countries. This will require a strong and integrated system for data dissemination at all levels (national and subnational) and for all sectors. 4.3.1 Proposed National Framework for Pre-Disaster Data Dissemination (NFPDD) The first step is setting up a national framework to monitor data needs and standards with the relevant disaster risk reduction stakeholders in each country. A National Frameworks for Pre-Disaster Data Dissemination (NFPDD) is a support framework to bring together disaster data specialists and users in a country. It will capture and validate pre-disaster data needs at the national and subnational level. It will also help coordinate the effective management of key issues such as data standards and metadata, data repositories, data quality, data updates, data relevancy, inter-organizational data sharing protocols and the interoperability of systems implemented by stakeholders. It is an advisory group. 4.3.1.1 Overview A small technical secretariat will support the activities of the NFPDD. The Secretariat shall meet regularly to examine issues, share progress and provides guidelines and advisory services to the national disaster risk community on issues related to data availability, data quality and data standards. If facilitates data sharing. This proposed framework doesn’t compete with existing data sharing platforms. Instead, it will facilitate the compilation and dissemination of pre-disaster data, and ex-ante sector or geographical analysis. It may be considered a specialized subset of a National Information Management System (NIMS)39, which includes a national statistical system with its components, and the national implementation of Spatial Data Infrastructure (SDI). There is a consensus among experts that the lack of timely and reliable data negatively affects the effectiveness of disaster interventions. Therefore, Expert-groups tend to endorse the approach of a coordinated national data dissemination as it increases the availability and quality of relevant datasets. The rapid and effective implementation of a national framework for pre-disaster data dissemination requires the establishment a national expert-group hosted by a specialized ministry, with a broad mandate, the established capacity and technical authority in data management for decision-making in disaster risk reduction. Therefore, the choice of the hosting institution is specific to the situation in each country. Some ministries include: Finance, Interior, Social Services, Emergencies and Agriculture. The national expert-group should prepare a national workplan presenting their annual goals, projects and services, resources and expected annual results in terms of increased national data management capacity and availability of pre-disaster data and situation analysis covering each disaster-prone area. A national disaster management agency (NDMA) may also host the workgroup on pre-disaster data, 39 The National Information Management system (NIMS) is an architecture to support data sharing and the implementation of data standards, data quality and trainings for data dissemination. African data experts endorsed the approach in Windhoek, June 6, 2007 (Declaration of Windhoek - DevInfo and National Information Management Systems), with the support of UNOCHA, UNICEF, UNFPA, WHO and UNDP. There are many other examples of expert-groups endorsing data sharing platforms. Page 32 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery although in such case it may require additional staffing to cope with the requirements. Ideally, a country should establish the workgroup without hiring new staff. The objective of the NFPDD is to help discover, collect, compile, verify and make available pre-disaster datasets in a country in a timely way. The NFPDD may organize technical trainings on data quality and dissemination. However, it mostly targets secondary data. It should not initiate primary data collection in the form of surveys. The collection and analysis of primary data should remain the responsibility of specialized sectoral groups, such as the humanitarian clusters and specialized agencies of ministries. Exceptionally, depending on its capacity, the NFPDD may facilitate a fast access to primary data from sensors such as remote sensing data, to help specialized groups. Also, as it maintains a complete repository of available data, and a detailed account of pre-disaster data needs and request, the NFPDD could support initiates aiming at addressing existing data gaps, particularly in areas and sectors presenting a lower coverage. 4.3.1.2 Implementation options for NFPDD No country was found that maintains a data coordination group focused on pre-disaster data. Instead, this is a typical responsibility of existing coordination groups for disaster risk reduction, especially during the preparedness and mitigation phases. However, given the importance of the need for pre- disaster datasets, a specialized coordination group may bring an added value to disaster preparedness and recovery. The first option is to support pre-disaster data collection and compilation activities within existing humanitarian clusters in developing countries. Humanitarian clusters are already collecting a substantive amount of data, although it doesn’t fully address the complexity of the needs for a comprehensive post disaster needs assessment that covers the emergency and long-term recovery, including complex macro-economic dimensions. Another option is to support a cross-sectoral thematic group of experts working together in a country to address the needs for pre-disaster datasets and situation analysis by geographic sectors of vulnerability to disaster. Such approach will benefit from the extensive experience of vulnerability analysis projects in countries affected by food-insecurity and presenting large hotspots of fragile livelihoods. Situation analysis in these countries will therefore include an early-warning dimension. There are many options to organize a small and agile group to manage sectoral data needs in a country. A feasibility study in each country will determine its specific implementation strategies. However, the common goal is not to setup a new structure. Instead, the objective is mobilizing the existing capacity to share their data needs and data resources following common standards, protocols and tools. Therefore, an updated dictionary of the PDNA indicators is the basis for implementation. 4.3.1.3 Implementation risks for NFPDD The successful implementation of a national framework for pre-disaster data dissemination must address two major risks. First, it should set feasible goals, based on a reasonable understanding of the country’s data capacity and resources. Also, to avoid a duplication of roles with existing data working group, it should promote the integration of efforts and resources. The quest for entropy is a high risk Page 33 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery for any data group. The well-established and reasonable data needs of the disaster risk reduction community should guide the implementation of a national framework for pre-disaster data dissemination. An all-data approach to cover all sectors and build a repository of all datasets in a country is an unnecessary risk. Doing so will be a duplication of effort, an unrealistic attempt to replace the national statistical data system or the spatial data infrastructure in a country. A small workgroup lacks the capacity to undertake such effort. 4.3.2 Proposed National Pre-Disaster Data Dissemination System (NPDDS) A data dissemination system is the technical backbone of a data workgroup. Many effective data platforms already exist, sharing disaster data. However, the need for a specialized layer of data services for pre-disaster data and analysis is fully justified. The National Pre-Disaster Data Dissemination System (NPDDS) is a proposal to address that need. Learning from experience, a cloud implementation is recommended for such system. Any commercial cloud platform that meets the requirements for a National Pre-Disaster Data Dissemination System (NPDDS) should also be affordable (for each country, the feasibility study could compare the cost of implementing Amazon web services AWS, Microsoft Azure, Google, IBM and other cloud services). A managed cloud solution offers a greater availability, security, redundancy and scalability of the database system. The system’s architecture is based around a national node, with remote access from the subnational level and a global replication node for redundancy and security. The national server offers (database, files and web services), it is mirrored by an international host. Server mirroring ensures data backup, business continuity, and faster disaster recovery. The main database services include a global data dictionary and specialized data repositories. These services are all accessible through a web-based interface including dashboards. It offers data upload and download. Technical support should be available globally and from the national node. In each country receiving technical support, it is recommended to establish a small support center that will manage technical resources and provide the basic services. The feasibility study for the National Pre-Disaster Data Dissemination System will help determine the optimal hardware and software for its national support center and subnational workgroups. It should explore Open source software solutions, taking into consideration the cost of local support. This aspect is often overlooked, as while Open source software is free, there is a financial for commercial support services. Page 34 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Page 35 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery V. LIST AND DEFINITIONS OF PDNA INDICATORS AND VARIABLES The scoping study has compiled a detailed table of indicators and variables used by PDNAs. Page 36 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery SECTOR SUB-SECTOR ADMIN-LEVEL TYPE NAME DEFINITION FORM UNIT National, Provincial, Sub- 1. Productive Agriculture DOC Calendar of production activities Agricultural calendar Provincial, Cities, Settlements National, Provincial, Sub- 2. Productive Agriculture VAR Cropped area per crop Cropped area per crop and season Quantitative Hectares Provincial, Cities, Settlements National, Provincial, Sub- 3. Productive Agriculture VAR Agricultural production per crop Agricultural production per crop Quantitative Kg or Ton Provincial, Cities, Settlements Total production of seasonal crops per crop (rice, National, Provincial, Sub- 4. Productive Agriculture VAR Seasonal crops vegetable, oilseed crops, culinary crops, corn, Quantitative Kg or Ton Provincial, Cities, Settlements legumes, others) Total production of permanent crops per crop National, Provincial, Sub- 5. Productive Agriculture VAR Permanent crops (coconut, coffee, cocoa, rubber, sugarcane, Quantitative Kg or Ton Provincial, Cities, Settlements cotton, tea, fruits, others) National, Provincial, Sub- 6. Productive Agriculture VAR Average yield per crop Average yield per crop Quantitative Kg/Hectare Provincial, Cities, Settlements National, Provincial, Sub- $ per 7. Productive Agriculture VAR Agricultural production cost per crop Production cost per crop Quantitative Provincial, Cities, Settlements hectare National, Provincial, Sub- 8. Productive Agriculture VAR Number of farmers or growers per crop Farmer or growers per gender and per crop Quantitative Number Provincial, Cities, Settlements National, Provincial, Sub- 9. Productive Agriculture VAR Crop unit price Crop price at farmgate, wholesale and retail levels Quantitative $ per unit Provincial, Cities, Settlements National, Provincial, Sub- 10. Productive Agriculture VAR Expected yield per crop Forecasted yield per crop (normal) Quantitative Kg/Hectare Provincial, Cities, Settlements National, Provincial, Sub- 11. Productive Agriculture VAR Forecasted volume of production per crop Forecasted agricultural production per crop Quantitative Kg or Ton Provincial, Cities, Settlements National, Provincial, Sub- Liters and $ 12. Productive Agriculture VAR Production of Honey Total production of honey Quantitative Provincial, Cities, Settlements value National, Provincial, Sub- Ton and $ 13. Productive Agriculture VAR Value-added agricultural products Total value-added agricultural products per type Quantitative Provincial, Cities, Settlements value National, Provincial, Sub- Liters and $ 14. Productive Agriculture VAR Processed vegetables Total production of processed vegetables Quantitative Provincial, Cities, Settlements value National, Provincial, Sub- Total production of processed fruits (juices, Liter, ton 15. Productive Agriculture VAR Processed fruits Quantitative Provincial, Cities, Settlements syrups, jam, cordial, others) and $ value National, Provincial, Sub- Total production of processed other processed Liter, ton 16. Productive Agriculture VAR Other processed foods Quantitative Provincial, Cities, Settlements food per type and $ value National, Provincial, Sub- 17. Productive Agriculture VAR Insured farmers Total number of insured farmers Quantitative Number Provincial, Cities, Settlements Agricultural physical assets (land, storage National, Provincial, Sub- buildings, animal shelter, rice mills, warehouse, Count and $ 18. Productive Agriculture VAR Agricultural physical assets Quantitative Provincial, Cities, Settlements public and private ownership, gender of value ownership, others) Agricultural equipment and machinery (tractor, National, Provincial, Sub- Count and $ 19. Productive Agriculture VAR Agricultural equipment and machinery reaper, flow, combined harvesters, dryers, seed Quantitative Provincial, Cities, Settlements value processing plants, other) Page 37 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Agricultural stock and raw materials (rice, National, Provincial, Sub- vegetable, oilseed crops, culinary crops, corn, Count and $ 20. Productive Agriculture VAR Agricultural stock and raw materials Quantitative Provincial, Cities, Settlements seeds, fertilizers, pesticides, veterinary supplies, value others) National, Provincial, Sub- 21. Productive Forestry VAR Production of timber Total production of timber per type Quantitative $/Hectare Provincial, Cities, Settlements National, Provincial, Sub- 22. Productive Forestry VAR Forest coverage natural and planted Total forest area Quantitative Hectare Provincial, Cities, Settlements National, Provincial, Sub- $/cubic 23. Productive Forestry VAR Timber price Timber unit price per type Quantitative Provincial, Cities, Settlements meter National, Provincial, Sub- 24. Productive Forestry VAR Orchards Total area of orchards (per type) Quantitative Hectare Provincial, Cities, Settlements National, Provincial, Sub- Number of animals (per type: cattle, pig, goat, 25. Productive Livestock VAR Animal stock (per type) Quantitative Year Provincial, Cities, Settlements sheep, buffalo, others) National, Provincial, Sub- 26. Productive Livestock VAR Unit prices paid to owners for animals Unit price paid to animal owners (per type) Quantitative Provincial, Cities, Settlements National, Provincial, Sub- Annual or monthly value of production of milk, 27. Productive Livestock VAR Production of milk, cheese, eggs, etc. Quantitative Provincial, Cities, Settlements cheese, eggs, etc. National, Provincial, Sub- 28. Productive Livestock VAR Price paid to producers Unit price paid to producers per product Quantitative Provincial, Cities, Settlements National, Provincial, Sub- 29. Productive Livestock VAR Meat production Total meat production per type Quantitative Kg or Ton Provincial, Cities, Settlements National, Provincial, Sub- 30. Productive Livestock VAR Unit price paid for meat products Unit price paid for meat products Quantitative $ per Kg Provincial, Cities, Settlements National, Provincial, Sub- 31. Productive Livestock VAR Milk production Total milk production per type Quantitative Liters Provincial, Cities, Settlements National, Provincial, Sub- 32. Productive Livestock VAR Leather production Total leather production Quantitative $ value Provincial, Cities, Settlements National, Provincial, Sub- Number of animals (per type: chicken, laying hens, 33. Productive Poultry VAR Poultry stock (per type) Quantitative Number Provincial, Cities, Settlements duck, turkey, others) National, Provincial, Sub- Total production of poultry meat (per type: 34. Productive Poultry VAR Production of poultry meat Quantitative Kg or Ton Provincial, Cities, Settlements chicken, duck, turkey, others) National, Provincial, Sub- 35. Productive Poultry VAR Poultry price Unit price paid to poultry producers per product Quantitative $ Provincial, Cities, Settlements National, Provincial, Sub- Number and 36. Productive Poultry VAR Production of eggs Total production of eggs Quantitative Provincial, Cities, Settlements $ value National, Provincial, Sub- 37. Productive Fishery VAR Boats number of boats per type and capacity Quantitative Number Provincial, Cities, Settlements Page 38 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery National, Provincial, Sub- 38. Productive Fishery VAR Nets number of nets per type Quantitative Number Provincial, Cities, Settlements National, Provincial, Sub- Monthly catch in volume or weight per type of 39. Productive Fishery VAR Monthly catch (in volume or weight) Quantitative Kg Provincial, Cities, Settlements fish catch National, Provincial, Sub- 40. Productive Fishery VAR Prices paid to fishermen Unit price per catch type Quantitative $/Kg Provincial, Cities, Settlements National, Provincial, Sub- 41. Productive Fishery VAR Inland fisheries Total area of inland fisheries Quantitative Hectare Provincial, Cities, Settlements National, Provincial, Sub- 42. Productive Fishery VAR Number of fish farmers Total number of fish farmers per type of fish Quantitative Number Provincial, Cities, Settlements National, Provincial, Sub- 43. Productive Fishery VAR Number of prawn farmers Total number of prawn farmers Quantitative Number Provincial, Cities, Settlements National, Provincial, Sub- 44. Productive Fishery VAR Number of inland fishers Total number of inland fishers Quantitative Number Provincial, Cities, Settlements National, Provincial, Sub- 45. Productive Fishery VAR Number of Sea fishers Total number of Sea fishers Quantitative Number Provincial, Cities, Settlements National, Provincial, Sub- 46. Productive Fishery VAR Cost of fishing tools Total value of fishing tools or assets Quantitative $ value Provincial, Cities, Settlements National, Provincial, Sub- 47. Productive Fishery VAR Prawn farming Total area of prawn farming Quantitative Hectare Provincial, Cities, Settlements National, Provincial, Sub- 48. Productive Fishery VAR Forecasted annual catch Total forecasted annual catch per type Quantitative Kg Provincial, Cities, Settlements Commerce and National, Provincial, Sub- 49. Productive DOC Commerce censuses or surveys Most recent commerce census or survey Quantitative Industry Provincial, Cities Commerce and Time series of commerce and trade volume and 50. Productive National, Provincial DOC Commerce and trade volume and prices Quantitative Industry prices Commerce and National, Provincial, Sub- 51. Productive DOC Small and medium enterprises Information on small and medium enterprises Quantitative Industry Provincial, Cities, Settlements Commerce and National, Provincial, Sub- 52. Productive VAR GDP by commerce Gross domestic product, by commerce categories Quantitative Industry Provincial, Cities Commerce and National, Provincial, Sub- Periodic surveys carried out by trade and industry 53. Productive DOC Surveys of commerce and industry Quantitative Industry Provincial, Cities ministries or by central bank Commerce and National, Provincial, Sub- 54. Productive DOC Industry census or survey Most recent industry census or survey Industry Provincial, Cities Commerce and National, Provincial, Sub- Time series of industry and trade volume and 55. Productive DOC Time series of industry and trade data Industry Provincial, Cities prices Commerce and National, Provincial, Sub- 56. Productive DOC Small and medium enterprises Information on small and medium enterprises Industry Provincial, Cities Commerce and National, Provincial, Sub- 57. Productive VAR GDP, by industry Gross domestic product, by industry categories Quantitative $ value Industry Provincial, Cities Commerce and National, Provincial, Sub- Number and size of industrial units (producers: Quantitative/Q Number and 58. Productive VAR Construction materials Industry Provincial, Cities cement, tiles, others) ualitative type Page 39 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Commerce and National, Provincial, Sub- Number and size of industrial units (producers: Quantitative/Q Number and 59. Productive VAR Beverage industries Industry Provincial, Cities beer, soft drinks, others) ualitative type Commerce and National, Provincial, Sub- Number and size of industrial units (producers: Quantitative/Q Number and 60. Productive VAR Chemical industries Industry Provincial, Cities pharmaceutical, paints, plastics, others) ualitative type Commerce and National, Provincial, Sub- Number and size of industrial units (producers: oil Quantitative/Q Number and 61. Productive VAR Other industries Industry Provincial, Cities and gas, garments, tobacco, automotive, others) ualitative type Commerce and National, Provincial, Sub- Total trading: cars, trucks, tractors, motorcycles, Number and 62. Productive VAR Trade data Quantitative Industry Provincial, Cities computers, others $ value Commerce and National, Provincial, Sub- Number and 63. Productive VAR Retail sector Number of formal retail units and trading data Quantitative Industry Provincial, Cities $ value Commerce and National, Provincial, Sub- Number of firms in the informal sector, type and Number and 64. Productive VAR Informal sector Quantitative Industry Provincial, Cities size: trading, services, food, others) $ value Commerce and National, Provincial, Sub- Number and size of firms in services (finance, Number and 65. Productive VAR Services Quantitative Industry Provincial, Cities repair shops, construction, restaurants, others) $ value National, Provincial, Sub- 66. Productive Tourism DOC Surveys of tourism sector Most recent survey of tourism sector Provincial, Cities National, Provincial, Sub- Time series of tourist arrivals, seasonality, and Number and 67. Productive Tourism DOC Tourist arrivals, seasonality, and income Quantitative Provincial, Cities income $ value National, Provincial, Sub- 68. Productive Tourism VAR Average length of stay Average length of stay per tourist Quantitative Number Provincial, Cities National, Provincial, Sub- 69. Productive Tourism VAR Average expenditure Average expenditures per tourist Quantitative $ value Provincial, Cities National, Provincial, Sub- Gross domestic product for tourism and 70. Productive Tourism VAR GDP tourism and subsectors Quantitative $ value Provincial, Cities subsectors Number of touristic assets, and average repair and Sub-Provincial, Cities, 71. Productive Tourism VAR Touristic assets replacement cost per type and rating: hotels, Quantitative Number settlements guest houses, resorts, spas, others. Number of natural and cultural touristic sites, Number, $ Sub-Provincial, Cities, Quantitative/q 72. Productive Tourism VAR Touristic sites protected natural areas, repair and replacement value and settlements ualitative cost of equipment per site, description of asset. narrative Page 40 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Inventory and description of sensitive and protected areas of important environmental, cultural and touristic value (public and private): maintenance of natural-systems, protected areas and ecosystems, strategic wildlife and highly diverse biological areas, areas of fish-farming and Sub-Provincial, Cities, 73. Productive Tourism VAR Sensitive areas and ecosystems animal raising, biological corridors and areas of qualitative narrative settlements seasonal importance to the feeding or reproduction of one or more species, Highly productive habitat for rare or endangered species (woodland, wetland, estuary, reef, etc.), Areas of aesthetic landscape and recreational value, others. Housing and Human National, Provincial, Sub- 74. Social DOC Household surveys Most recent household surveys Settlements Provincial, Cities Housing and Human National, Provincial, Sub- 75. Social DOC Housing census Most recent housing census Settlements Provincial, Cities Number of houses per type: shanties, wood single floor, wood two-floor and above, reinforced concrete and wood single-floor, reinforced Housing and Human National, Provincial, Sub- concrete and wood multiple floors, reinforced 76. Social VAR Housing stock per type Quantitative Settlements Provincial, Cities concrete and brick pillars, bricks and reinforced concrete pillars, others), average number of occupants (female, male), number of units for rent, etc. Housing and Human National, Provincial, Sub- 77. Social VAR Monthly rentals Average value of monthly rental per unit type Quantitative Settlements Provincial, Cities Housing and Human National, Provincial, Sub- Average construction cost and replacement value 78. Social VAR Construction cost Quantitative Settlements Provincial, Cities per housing unit type Housing and Human National, Provincial, Sub- Description of the local construction sector 79. Social VAR Construction sector capacity Qualitative Settlements Provincial, Cities capacity for recovery and support needs Housing and Human National, Provincial, Sub- Costs of typical furniture and equipment in the 80. Social VAR Price of typical furniture and equipment Quantitative Settlements Provincial, Cities, Settlements affected area, by dwelling type Number of dwellings in the affected area, Housing and Human National, Provincial, Sub- specifying for each whether they are rural or 81. Social VAR Number of dwellings Quantitative Settlements Provincial, Cities, Settlements urban, single- or multi-family, owned by men or women, privately or publicly owned Page 41 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Quality of existing dwellings, broken down either by permanent versus temporary units, the type of Housing and Human National, Provincial, Sub- construction materials used (reinforced concrete, 82. Social VAR Type of existing dwellings Quantitative Settlements Provincial, Cities, Settlements brick, wood, adobe, cardboard, etc.), the degree of conservation (good, regular, poor, etc.) or the type of dwelling (house, mobile home, shack, etc.) Average dwelling size by type, considering the Housing and Human National, Provincial, Sub- 83. Social VAR Average dwelling size average number of inhabitants per unit and the Quantitative Settlements Provincial, Cities, Settlements average area in square meters Housing and Human National, Provincial, Sub- Main construction techniques and The main construction techniques and materials 84. Social VAR Quantitative Settlements Provincial, Cities, Settlements materials used in the affected area Number of educational premises existing in the affected area, classified into urban and rural, National, Provincial, Sub- 85. Social Education VAR Number of educational premises publicly and privately owned and educational Quantitative Provincial, Cities, Settlements level (primary, secondary or middle, technical and vocational, university). Number of classrooms, teachers and students – National, Provincial, Sub- total and gender, for example, per morning, 86. Social Education VAR Number of classrooms, students & teachers Quantitative Provincial, Cities, Settlements afternoon and evening shift– for each educational premise Quality of the building of the premises, based on – for example– the type of construction materials National, Provincial, Sub- 87. Social Education IND Quality of the building of the premises used (adobe, wood, brick, concrete, etc.), the Qualitative Provincial, Cities, Settlements average age of the construction and its degree of maintenance Number of educational facilities per type, average number of students (public/private), average Sub-Provincial, Cities, Number, $ 88. Social Education IND Educational facilities replacement and repair cost: kindergarten, pre- Quantitative Settlements value school, primary, secondary, university, vocational training institutes, others. Furnishings and equipment typical of educational National, Provincial, Sub- Number, $ 89. Social Education VAR Furnishings and equipment centers in accordance with previously defined Quantitative Provincial, Cities, Settlements value categories National, Provincial, Sub- Average education fee per type (public/private), 90. Social Education VAR Education fees Quantitative $ value Provincial, Cities, Settlements forecasted monthly to annual revenue The socio-demographic situation and the status of the main epidemiological indicators, including the National, Provincial, Sub- Socio-demographics and epidemiological 91. Social Health VAR morbidity rate and Quantitative Provincial, Cities, Settlements data incidence of different diseases that are relevant to the type of disaster in question Page 42 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Number of medical facilities per type (public/private), average replacement and repair National, Provincial, Sub- Number, $ 92. Social Health VAR Health-care facilities costs, average daily visits (male, female, children Qualitative Provincial, Cities, Settlements value and adults): health clinics, hospitals, medical laboratories, others. Number of medical personnel per type, operation National, Provincial, Sub- Human resources, equipment and medical 93. Social Health VAR and replacement value of equipment and medical Quantitative Provincial, Cities, Settlements supplies supplies National, Provincial, Sub- Description of the sector’s management, the way 94. Social Health VAR Management, finance and funding Qualitative Provincial, Cities, Settlements in which it is financed and its financial resources Description of the state of health service coverage National, Provincial, Sub- 95. Social Health IND Coverage of health services provided by type of organization, including assets, Qualitative Provincial, Cities, Settlements strengths and gaps. The unit cost of the services supplied, including National, Provincial, Sub- 96. Social Health VAR Cost of health services the cost of a doctor’s visit, daily hospital room Quantitative Provincial, Cities, Settlements charges and average wages, among others Number, average repair and replacement costs of Sub-Provincial, Cities, key local equipment and supply: CT scan, X-ray 97. Health IVAR Local medical equipment and supply Quantitative $ value Settlements machinery, MRI machinery, Sonar machinery, medicines, furniture, others. Number and characteristics of public and private historic heritage assets – broken down into the categories of world heritage, heritage buildings, National, Provincial, Sub- museums, archaeological sites, movable goods, 98. Social Cultural heritage VAR Public and private historic heritage assets Quantitative Provincial, Cities, Settlements archives or documentary collections heritage churches, houses located in historic centers, libraries and collections located in foundations, libraries and churches Quality of construction of the above premises, based on –for example– the type of construction materials used (adobe, wood, brick, concrete, National, Provincial, Sub- Quality of construction of the heritage 99. Social Cultural heritage VAR etc.), the age of the construction and its degree of Qualitative Provincial, Cities, Settlements assets maintenance; - Furnishings and equipment typical of heritage centers in accordance with previously defined categories National, Provincial, Sub- 100. Social Cultural heritage VAR Costs of building, furniture and equipment Unit costs of building, furniture and equipment Quantitative Provincial, Cities, Settlements Organization of the entire water supply subsector: Drinking water and National, Provincial, Sub- 101. Infrastructure DOC Water supply framework service provider utilities, municipalities and sanitation Provincial, Cities, Settlements regulatory and governing bodies Page 43 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Breakdown of the population served by collective Drinking water and National, Provincial, Sub- Population served by collective and 102. Infrastructure VAR and individual systems (such as collective water Quantitative sanitation Provincial, Cities, Settlements individual water systems systems, individual wells, multi-family systems) Pre-disaster water service coverage levels (urban Drinking water and National, Provincial, Sub- 103. Infrastructure IND Water service coverage and rural: residential, commercial, industrial, Quantitative sanitation Provincial, Cities, Settlements others) Costs of materials, construction, equipment, Drinking water and National, Provincial, Sub- 104. Infrastructure VAR Water infrastructure costs chemicals/reagents and other inputs required for Quantitative sanitation Provincial, Cities, Settlements the rehabilitation and reconstruction of systems Population served before the disaster (number of Drinking water and National, Provincial, Sub- 105. Infrastructure VAR Population receiving water domestic connections, average levels of water Quantitative sanitation Provincial, Cities, Settlements consumption, etc.) Number, capacity, operating cost, average repair Drinking water and National, Provincial, Sub- cost and average replacement cost of: treatment 106. Infrastructure VAR Water supply structures Quantitative sanitation Provincial, Cities, Settlements plants, storage, distribution and other sub- systems Drinking water and National, Provincial, Sub- Number per type, average replacement cost, unit Number and 107. Infrastructure VAR Water supply equipment Quantitative sanitation Provincial, Cities, Settlements repair costs $ value Number of: open well, close well with hand pump, close well with storage and electric pump and tap Drinking water and Sub-Provincial, Cities, Number and 108. Infrastructure VAR Water supply type stand, others, public and private ownership, Quantitative sanitation Settlements $ value number of household beneficiaries, average construction and repair costs Drinking water and National, Provincial, Sub- Water supply rates, existing subsidies, billing 109. Infrastructure VAR Water rates Quantitative sanitation Provincial, Cities, Settlements collection effectiveness Drinking water and National, Provincial, Sub- Water supply subsidies, billing collection 110. Infrastructure VAR Water subsidies Quantitative sanitation Provincial, Cities, Settlements effectiveness Drinking water and National, Provincial, Sub- 111. Infrastructure IND Water supply effectiveness Water supply collection effectiveness Quantitative sanitation Provincial, Cities, Settlements Drinking water and National, Provincial, Sub- 112. Infrastructure DOCS Blueprints of all affected systems Blueprints of all affected systems sanitation Provincial, Cities, Settlements Drinking water and National, Provincial, Sub- Water construction techniques and Construction techniques and materials used in the 113. Infrastructure DOCS sanitation Provincial, Cities, Settlements materials systems’ components Drinking water and National, Provincial, Sub- Sewage services, geographical coverage and 114. Infrastructure VAR Access to sewage disposal services Quantitative sanitation Provincial, Cities, Settlements beneficiaries of services before the disaster Drinking water and National, Provincial, Sub- Sewage disposal rates, subsidies and billing 115. Infrastructure VAR Sewage disposal rates Quantitative sanitation Provincial, Cities, Settlements effectiveness Number of households with access to solid waste Drinking water and National, Provincial, Sub- Access to solid waste collection and 116. Infrastructure VAR collection services, geographical coverage and Quantitative sanitation Provincial, Cities, Settlements disposal services beneficiaries of services before the disaster Page 44 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery National, Provincial, Sub- Number, location and description of electrical 117. Infrastructure Electricity VAR Energy production Quantitative Provincial, Cities, Settlements power generation assets Location and distribution of power transmission National, Provincial, Sub- 118. Infrastructure Electricity VAR Energy distribution assets: transmission lines, substations, and Quantitative Provincial, Cities, Settlements distribution grids National, Provincial, Sub- 119. Infrastructure Electricity VAR Power grid performance Performance projections Quantitative Provincial, Cities, Settlements National, Provincial, Sub- 120. Infrastructure Electricity VAR Electricity coverage Expected growth rate for energy sales, per year Quantitative Provincial, Cities, Settlements National, Provincial, Sub- Historical sales, by volume and rate, for all user 121. Infrastructure Electricity VAR Electricity rates Quantitative Provincial, Cities, Settlements sectors Types of roads (concrete, asphalt, bituminous, National, Provincial, Sub- graveled, earth, other): length, average Km, $ value, 122. Infrastructure Transport VAR Road network Quantitative Provincial, Cities, Settlements replacement cost, average repair cost, average Number number of users per month (persons and vehicles) National, Provincial, Sub- 123. Infrastructure Transport VAR Bus and taxi stations Location and description of bus and taxi stations Quantitative Provincial, Cities, Settlements Number of bridges and tunnels per type (steel, National, Provincial, Sub- concrete, wood, other): average replacement Number, $ 124. Infrastructure Transport VAR Bridges and tunnels Quantitative Provincial, Cities, Settlements cost, average repair cost, average number of users value per month (persons and vehicles) Number and types of transport related buildings National, Provincial, Sub- (single floor, 2-5, 6-10, over 10 floors and others): Number, $ 125. Infrastructure Transport VAR Transport buildings Quantitative Provincial, Cities, Settlements average repair and replacement cost of building, value, $/sqm square meter of roof, wall, floor, others; National, Provincial, Sub- 126. Infrastructure Transport VAR Railways Railroad tracks, station buildings, signals Quantitative Provincial, Cities, Settlements National, Provincial, Sub- 127. Infrastructure Transport VAR Airports Airports, airfields and buildings Quantitative Provincial, Cities, Settlements National, Provincial, Sub- 128. Infrastructure Transport VAR Ports Location and description of port infrastructure Quantitative Provincial, Cities, Settlements National, Provincial, Sub- 129. Infrastructure Transport VAR Waterways Rivers and waterways Quantitative Provincial, Cities, Settlements National, Provincial, Sub- Unit operational costs for transport on persons 130. Infrastructure Transport VAR Transportation costs Quantitative Provincial, Cities, Settlements and cargo, for different modes of transportation National, Provincial, Sub- Traffic information into/out of affected areas, pre- 131. Infrastructure Transport VAR Traffic Quantitative Provincial, Cities, Settlements and post-disaster Total number, average acquisition value per unit, National, Provincial, Sub- average replacement and repair cost of heavy Number, $ 132. Infrastructure Transport VAR Heavy transport equipment Quantitative Provincial, Cities, Settlements transport equipment: bulldozers, graders, loaders, value trucks, others Page 45 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Total number, average acquisition value per unit, National, Provincial, Sub- average replacement and repair cost of other Number, $ 133. Infrastructure Transport VAR Other transport equipment Quantitative Provincial, Cities, Settlements transport equipment: communication, security, value others Total value, average acquisition value per unit, National, Provincial, Sub- average replacement and repair cost of Number, $ 134. Infrastructure Transport VAR Transportation materials and supplies Quantitative Provincial, Cities, Settlements transportation materials and supplies: furniture, value computers, others Number, total value, average operation, replacement and repair cost of air transportation National, Provincial, Sub- equipment (public/private): airplanes, helicopters Number, $ 135. Infrastructure Transport VAR Air transportation assets Quantitative Provincial, Cities, Settlements and other aircrafts, building structures by type, value equipment and machinery (navigation equipment, baggage handling, security equipment, others) Number, total value, average operation, replacement and repair cost of waterway transportation equipment (public/private): ships, National, Provincial, Sub- Number, $ 136. Infrastructure Transport VAR Waterway transportation assets ferries, and other watercrafts, building structures Quantitative Provincial, Cities, Settlements value by type, equipment and machinery (navigation equipment, baggage handling, security equipment, others) Number, total value, average operation, replacement and repair cost of railway transportation equipment (public/private): National, Provincial, Sub- Number, $ 137. Infrastructure Transport VAR Railway transportation assets locomotives, trains, coaches, rails and other, Quantitative Provincial, Cities, Settlements value building structures by type, equipment and machinery (navigation equipment, baggage handling, security equipment, others) Community National, Provincial, Sub- Number and total length of internal roads, 138. Infrastructure VAR Connective infrastructure Quantitative Number, Km Infrastructure Provincial, Cities, Settlements walkways, footpaths within the community Total Length, Area and Cost, Cost per unit of small-scale and low-cost protecting structures built for various community purposes, including Community National, Provincial, Sub- Ha, Meters, 139. Infrastructure VAR Protective Infrastructure drainage structures, pipe culverts, box culverts, Quantitative Infrastructure Provincial, Cities, Settlements $ value footbridges, retaining walls, protection of slopes, jetties, small embankments or protection walls, and small earthen dams Total area, length, and cost, cost per unit of small marketplaces and infrastructure within market Community National, Provincial, Sub- Ha, Meters, 140. Infrastructure VAR Socioeconomic structures grounds, including pathways, sheds, drains, Quantitative Infrastructure Provincial, Cities, Settlements $ value community shops, community resource centers, religious centers, graveyards, playgrounds Page 46 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Total area, length, and cost, cost per meter unit of water reservoirs and water sources, supply pipes, Ha, Meters, Community National, Provincial, Sub- 141. Infrastructure IND Water and Sanitation Lifelines ponds, the community water supply system, Quantitative Cubic Infrastructure Provincial, Cities, Settlements pump houses and deep tube wells, drainage lines, meters, $ waste disposal and composting plants Total number, size and cost of biogas plants, bio- Number, Ha, Community National, Provincial, Sub- gasifiers, solar home systems for electrification, 142. Infrastructure IND Energy Lifelines Quantitative Cubic Infrastructure Provincial, Cities, Settlements and similar community-driven low-cost technical meters, Kwh plants Total number, size and cost of community Community National, Provincial, Sub- telephone centers, community-based early 143. Infrastructure IND Communication Lifelines Quantitative Infrastructure Provincial, Cities, Settlements warning systems and communication devices, community-run radio and communication systems Total beneficiaries of each community Community National, Provincial, Sub- infrastructures - Including population coverage, 144. Infrastructure IND Coverage of services Quantitative Number Infrastructure Provincial, Cities, Settlements type of user, of each component of community infrastructure Number, location, replacement value and description of telecommunication installations in National, Provincial, Sub- Quantitative/q Number, $ 145. Infrastructure Communications DOC Telecommunication assets the affected areas (aerial telephone lines and Provincial, Cities, Settlements ualitative value poles, electronic equipment and components, number of consumers) Number, location and description of postal and National, Provincial, Sub- Number, $ 146. Infrastructure Communications VAR Postal services courier service buildings and other assets in the Quantitative Provincial, Cities, Settlements value affected area National, Provincial, Sub- Number and replacement value of Number, $ 147. Infrastructure Communications VAR Telecommunication buildings Quantitative Provincial, Cities, Settlements telecommunication buildings value National, Provincial, Sub- 148. Infrastructure Communications VAR Telecommunication revenues Annual revenues or sales, per enterprise Quantitative $ value Provincial, Cities, Settlements Number, size and types of government buildings, National, Provincial, Sub- Number, $ 149. Cross sectoral Governance VAR Building assets warehouses, garage, and others: average cost of Quantitative Provincial, Cities, Settlements value, $/sqm building, square meter of roof, wall, floor, others; Number and types of equipment, machinery and supplies in government buildings: average National, Provincial, Sub- Number, $ 150. Cross sectoral Governance VAR Building equipment assets acquisition and replacement value, per unit: Quantitative Provincial, Cities, Settlements value elevators, computers and other IT assets, air conditioners, furniture, others. Page 47 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Existing sectoral laws and regulations, public financial management related regulations, legal provisions for aid management, decentralization framework, civil-military mandates, citizenship National, Provincial, Sub- and property laws, existing tensions and conflicts, 151. Cross sectoral Governance DOC Legal and institutional context Qualitative Provincial, Cities, Settlements conditions related to transparency, human rights abuses, crime statistics, gender-based violence, discrimination based on sex, age, ethnicity, religion, caste, etc. Capacity and gap analysis. Police force per capita, prison population, % citizens without ID papers, % undocumented National, Provincial, Sub- property ownership, reported human rights Quantitative 152. Cross sectoral Governance IND Public records Provincial, Cities, Settlements abuses, surveys on insecurity, access to justice Qualitative indicators for disadvantaged and marginalized groups % state budget expanded through local governments (recurrent/capital), % execution rate of local investment budgets, % of local investment National, Provincial, Sub- Quantitative 153. Cross sectoral Governance DOC Local governance resources funded from own-revenues (taxes and Provincial, Cities, Settlements Qualitative other sources), trends in central government transfers, service delivery figures for other local government services. Capacity and gap analysis Data on gender equality and the position of women regarding specific indicators (birth, adolescent fertility rate, death, injury, labour and National, Provincial, Sub- Gender equality demographics 154. Cross sectoral Gender IND income, employment, schooling, labour market Quantitative Provincial, Cities, Settlements participation, participation in decision making, home and land ownership, debt, access, control and use of resources. National, Provincial, Sub- 155. Cross sectoral Gender DOC Gender equality context Policy frameworks and Laws on gender equality Qualitative Provincial, Cities, Settlements Productive, reproductive and community National, Provincial, Sub- Roles of women, girls, boys and men in the 156. Cross sectoral Gender DOC activities, educational activities, informal income Provincial, Cities, Settlements community generation activities, etc. Page 48 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Gender-based violence [GBV], trafficking, feminization of poverty, limited mobility, migration patterns by sex, access to services, excluded and/or disadvantaged groups National, Provincial, Sub- Structural gender- (minorities, people living with HIV/AIDS) by sex, 157. Cross sectoral Gender IND Provincial, Cities, Settlements based risks and vulnerabilities negative coping mechanisms to crisis, food insecurity by sex; unpaid work trends by sex, seasonal employment, harmful cultural practices (e.g. early marriage, female genital mutilation, widow cleansing, etc.) Number, location and description of environmental assets (national park, heritage National, Provincial, Sub- sites, nature reserves, hunting reserves, marine 158. Cross sectoral Environment VAR Environmental assets Qualitative Provincial, Cities, Settlements reserves, wetlands, wildlife corridors, ecotourism, endemic species, watersheds, sources of drinking water, etc. and ecosystem services) National, Provincial, Sub- Location and description of sensitive ecosystem in 159. Cross sectoral Environment VAR Sensitive ecosystems Qualitative Provincial, Cities, Settlements affected areas National, Provincial, Sub- Location and description of hazardous sites and Quantitative 160. Cross sectoral Environment VAR Hazardous sites Provincial, Cities, Settlements hazardous materials and Qualitative National, Provincial, Sub- Laws, regulations and preparedness plans for Quantitative 161. Cross sectoral Environment DOC Hazardous site plans Provincial, Cities, Settlements hazardous sites. and Qualitative National, Provincial, Sub- Name, location and description of environmental 162. Cross sectoral Environment VAR Environmental infrastructure Quantitative Provincial, Cities, Settlements infrastructure Cross sectoral Employment and National, Provincial, Sub- IND Employment by household Labour force, Employment rate per household and Quantitative 163. Livelihood Provincial, Cities, Settlements gender Cross sectoral Employment and National, Provincial, Sub- VAR Unemployment rate by household Labour force, unemployment rate per household Quantitative % 164. Livelihood Provincial, Cities, Settlements Cross sectoral Employment and National, Provincial, Sub- VAR Self-employment rate by household Labour force, self-employment, wage Quantitative % 165. Livelihood Provincial, Cities, Settlements employment, Cross sectoral Employment and National, Provincial, Sub- VAR Wage employment by household and Labour force, self-employment, Quantitative 166. $ Livelihood Provincial, Cities, Settlements gender Cross sectoral Employment and National, Provincial, Sub- IND Sources of income by gender Salary, farms, commerce, real estate, transfers, Quantitative 167. Livelihood Provincial, Cities, Settlements pensions, remittances, household assets and non- labor income Cross sectoral Employment and National, Provincial, Sub- VAR household assets Description and value of household assets Quantitative 168. Livelihood Provincial, Cities, Settlements and Qualitative Cross sectoral Employment and National, Provincial, Sub- VAR Non-labor income by household and Non-labor income Quantitative 169. Livelihood Provincial, Cities, Settlements gender and Qualitative Cross sectoral Employment and National, Provincial, Sub- IND Livelihood Vulnerability Overall indicator of household vulnerability Quantitative 170. Livelihood Provincial, Cities, Settlements and existing coping mechanisms Page 49 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Cross sectoral Employment and National, Provincial, Sub- VAR Poor smallholder farming households Livelihood Vulnerability, number of poor Quantitative 171. Number Livelihood Provincial, Cities, Settlements smallholder farming households Cross sectoral Employment and National, Provincial, Sub- VAR Households depending on informal self- Livelihood Vulnerability, number of households Quantitative 172. Number Livelihood Provincial, Cities, Settlements employed workers depending on informal self-employed workers Cross sectoral Employment and National, Provincial, Sub- VAR Households depending on casual or short- Livelihood Vulnerability, number of households Quantitative 173. Number Livelihood Provincial, Cities, Settlements term wage jobs depending on casual or short-term wage jobs Cross sectoral Employment and National, Provincial, Sub- VAR Households with assisted elderly or Livelihood Vulnerability, number of households Quantitative 174. Number Livelihood Provincial, Cities, Settlements disabled people with assisted elderly or disabled people Cross sectoral Employment and National, Provincial, Sub- VAR households composed of ethnic or religious Livelihood Vulnerability, number of households Quantitative 175. Number Livelihood Provincial, Cities, Settlements minorities composed of ethnic or religious minorities Cross sectoral Employment and National, Provincial, Sub- VAR Female-headed households with young Livelihood Vulnerability, female-headed Quantitative 176. Livelihood Provincial, Cities, Settlements children or elderly relatives in their care households with young children or elderly Number relatives in their care Existing institutions and agencies responsible for DRR, Existing policies, programs and/or plans to National, Provincial, Sub- 177. Cross sectoral Disaster Risk Reduction DOC Institutions and Policies for DRR integrate DRR into recovery, Existing mechanisms Provincial, Cities, Settlements to mainstream DRR, and Responsibilities of local authorities National, Provincial, Sub- Existing laws, regulations, orders related to DRR 178. Cross sectoral Disaster Risk Reduction DOC Legislation and regulations for DRR Provincial, Cities, Settlements and their effectiveness National, Provincial, Sub- Inventory of DRR-related government 179. Cross sectoral Disaster Risk Reduction VAR Location, number, size, capacity of buildings Quantitative Number Provincial, Cities, Settlements buildings, Pre-disaster staffing and human resource National, Provincial, Sub- 180. Cross sectoral Disaster Risk Reduction VAR Human resources for DRR capacities per profile and gaps, location and Quantitative Number Provincial, Cities, Settlements number of volunteers Annual public budget for DRR, local budget for National, Provincial, Sub- 181. Cross sectoral Disaster Risk Reduction VAR Financial resources for DRR DRR, external resources, private sector and civil Quantitative $, % Provincial, Cities, Settlements society contributions Level of participation of civil society (NGOs, National, Provincial, Sub- 182. Cross sectoral Disaster Risk Reduction DOC Participation and inclusion to DRR activities community-based organizations, civil society Provincial, Cities, Settlements groups including women’s organizations, etc.) Existing early warning, weather forecasting and risk information systems and their effectiveness, Government´s knowledge and previous National, Provincial, Sub- 183. Cross sectoral Disaster Risk Reduction DOC Risk and Vulnerability Information experience with DRR, Statistical information on Provincial, Cities, Settlements disaster occurrence and their effects and impacts, sex-disaggregated data impacts and losses, National and local risk assessments, Page 50 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery Scientific data on hazard exposure (including probability, frequency, intensity and scale), Available hazard, vulnerability and/or risk maps or studies, Data on determinants of conditions of vulnerability; e.g. poverty indicators, National, Provincial, Sub- Number 184. Cross sectoral Disaster Risk Reduction IND Hazard Exposure gender indicators and analysis, population growth Qualitative Provincial, Cities, Settlements % rate, cutting of forests and mangroves (% per year), livestock patterns, building vulnerability analysis, people’s awareness about hazard etc., Systems of indicators of disaster risk and vulnerability at national and sub-national scales Studies or evaluations about disaster risk issues and the functioning of DRR system, Past/existing programs on DRR capacity development and future plans, Response analysis of how National, Provincial, Sub- government, NGOs, civil society, international 185. Disaster Risk Reduction DOC Past Experience and Lessons Learned Qualitative Provincial, Cities, Settlements organisations, private sector and communities responded to past disasters –strengths and weaknesses of response; and Previous evaluations, studies and lessons-learned exercises relating to past recovery programs VAR: Variable IND: Indicator DOC: Document Page 51 of 63 SCOPING STUDY - Pre-Disaster Baseline Datasets for Quick, Effective and Coordinated Disaster Assessment and Recovery VI. BIBLIOGRAPHY Abualkhair, H., Lodree, E. J., & Davis, L. B. (2019). Managing volunteer convergence at disaster relief centers. International Journal of Production Economics. Adams, C., & Neef, A. (2019). Patrons of disaster: The role of political patronage in flood response in the Solomon Islands. World Development Perspectives. Aghapour, A. H., Yazdani, M., Jolai, F., & Mojtahedi, M. (2019). Capacity planning and reconfiguration for disaster-resilient health infrastructure. 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